PERF: Use reshape instead of ravel/flatten

pandas/core/arrays/arrow/array.py

@@ -2970,7 +2970,7 @@ def transpose_homogeneous_pyarrow(
     """
     arrays = list(arrays)
     nrows, ncols = len(arrays[0]), len(arrays)
-    indices = np.arange(nrows * ncols).reshape(ncols, nrows).T.flatten()
+    indices = np.arange(nrows * ncols).reshape(ncols, nrows).T.reshape(-1)
     arr = pa.chunked_array([chunk for arr in arrays for chunk in arr._pa_array.chunks])
     arr = arr.take(indices)
     return [ArrowExtensionArray(arr.slice(i * ncols, ncols)) for i in range(nrows)]

pandas/core/generic.py

@@ -9271,7 +9271,9 @@ def compare(
 
         # reorder axis to keep things organized
         indices = (
-            np.arange(diff.shape[axis]).reshape([2, diff.shape[axis] // 2]).T.flatten()
+            np.arange(diff.shape[axis])
+            .reshape([2, diff.shape[axis] // 2])
+            .T.reshape(-1)
         )
         diff = diff.take(indices, axis=axis)
 

pandas/core/indexing.py

@@ -2440,7 +2440,7 @@ def _align_frame(self, indexer, df: DataFrame) -> DataFrame:
                 ax = self.obj.axes[i]
                 if is_sequence(ix) or isinstance(ix, slice):
                     if isinstance(ix, np.ndarray):
-                        ix = ix.ravel()
+                        ix = ix.reshape(-1)
                     if idx is None:
                         idx = ax[ix]
                     elif cols is None:

pandas/core/reshape/reshape.py

@@ -842,7 +842,7 @@ def _convert_level_number(level_num: int, columns: Index):
                     [x._values.astype(dtype, copy=False) for _, x in subset.items()]
                 )
                 N, K = subset.shape
-                idx = np.arange(N * K).reshape(K, N).T.ravel()
+                idx = np.arange(N * K).reshape(K, N).T.reshape(-1)
                 value_slice = value_slice.take(idx)
             else:
                 value_slice = subset.values
@@ -924,7 +924,7 @@ def _reorder_for_extension_array_stack(
     # idx is an indexer like
     # [c0r0, c1r0, c2r0, ...,
     #  c0r1, c1r1, c2r1, ...]
-    idx = np.arange(n_rows * n_columns).reshape(n_columns, n_rows).T.ravel()
+    idx = np.arange(n_rows * n_columns).reshape(n_columns, n_rows).T.reshape(-1)
     return arr.take(idx)